{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取用户购买纪录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 54772 entries, 0 to 54771\n",
      "Data columns (total 6 columns):\n",
      "Dates       54772 non-null datetime64[ns]\n",
      "Action      54772 non-null object\n",
      "User        54772 non-null object\n",
      "Product     54772 non-null object\n",
      "Quantity    54772 non-null int64\n",
      "Price       54721 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(1), int64(1), object(3)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "m_cols = ['Time','Action','User','Product', 'Quantity','Price']\n",
    "orders = pd.read_csv('Data/purchase_order.tab', sep='\\t',\n",
    "            parse_dates={'Dates': [0]},names=m_cols, encoding='utf-8')\n",
    "orders.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dates</th>\n",
       "      <th>Action</th>\n",
       "      <th>User</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-07-01 00:00:01</td>\n",
       "      <td>order</td>\n",
       "      <td>U312622727</td>\n",
       "      <td>P0006944501</td>\n",
       "      <td>1</td>\n",
       "      <td>1069.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-07-01 00:00:03</td>\n",
       "      <td>order</td>\n",
       "      <td>U239012343</td>\n",
       "      <td>P0006018073</td>\n",
       "      <td>1</td>\n",
       "      <td>1680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-07-01 00:00:19</td>\n",
       "      <td>order</td>\n",
       "      <td>U10007697373</td>\n",
       "      <td>P0002267974</td>\n",
       "      <td>1</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-07-01 00:01:10</td>\n",
       "      <td>order</td>\n",
       "      <td>U296328517</td>\n",
       "      <td>P0016144236</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-07-01 00:01:36</td>\n",
       "      <td>order</td>\n",
       "      <td>U300884570</td>\n",
       "      <td>P0014516980122</td>\n",
       "      <td>1</td>\n",
       "      <td>249.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Dates Action          User         Product  Quantity   Price\n",
       "0 2015-07-01 00:00:01  order    U312622727     P0006944501         1  1069.0\n",
       "1 2015-07-01 00:00:03  order    U239012343     P0006018073         1  1680.0\n",
       "2 2015-07-01 00:00:19  order  U10007697373     P0002267974         1   285.0\n",
       "3 2015-07-01 00:01:10  order    U296328517     P0016144236         1   550.0\n",
       "4 2015-07-01 00:01:36  order    U300884570  P0014516980122         1   249.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 探索用户购买纪录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       P0006944501\n",
       "1       P0006018073\n",
       "2       P0002267974\n",
       "3       P0016144236\n",
       "4    P0014516980122\n",
       "Name: Product, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['Product'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       8.0\n",
       "mean     1069.0\n",
       "std         0.0\n",
       "min      1069.0\n",
       "25%      1069.0\n",
       "50%      1069.0\n",
       "75%      1069.0\n",
       "max      1069.0\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders['Product'] == 'P0006944501']['Price'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1069.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders['Product'] == 'P0006944501']['Price'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['P0006944501', 'P0006018073', 'P0002267974', ..., 'P0022884606',\n",
       "       'P0013911085', 'P0025123755'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['Product'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20054"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(orders['Product'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "P0000005913       1590.0\n",
       "P0000006020       1290.0\n",
       "P0000006591        999.0\n",
       "P0000007744       3280.0\n",
       "P0000008190011    5083.0\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select Product, avg(Price) from orders \n",
    "# group by Product limit 5\n",
    "\n",
    "orders.groupby('Product')['Price'].mean().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "P0000143511    438888.0\n",
       "P0000143500    438888.0\n",
       "P0006584093    320000.0\n",
       "P0025280275    183900.0\n",
       "P0000150006    111375.0\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select Product, avg(Price) from orders \n",
    "# group by Product order by avg(Price) desc limit 5\n",
    "\n",
    "orders.groupby('Product')['Price'].mean()\\\n",
    ".sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "orders['Total_Price'] = orders['Quantity'] * orders['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dates</th>\n",
       "      <th>Action</th>\n",
       "      <th>User</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "      <th>Total_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-07-01 00:00:01</td>\n",
       "      <td>order</td>\n",
       "      <td>U312622727</td>\n",
       "      <td>P0006944501</td>\n",
       "      <td>1</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1069.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-07-01 00:00:03</td>\n",
       "      <td>order</td>\n",
       "      <td>U239012343</td>\n",
       "      <td>P0006018073</td>\n",
       "      <td>1</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-07-01 00:00:19</td>\n",
       "      <td>order</td>\n",
       "      <td>U10007697373</td>\n",
       "      <td>P0002267974</td>\n",
       "      <td>1</td>\n",
       "      <td>285.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-07-01 00:01:10</td>\n",
       "      <td>order</td>\n",
       "      <td>U296328517</td>\n",
       "      <td>P0016144236</td>\n",
       "      <td>1</td>\n",
       "      <td>550.0</td>\n",
       "      <td>550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-07-01 00:01:36</td>\n",
       "      <td>order</td>\n",
       "      <td>U300884570</td>\n",
       "      <td>P0014516980122</td>\n",
       "      <td>1</td>\n",
       "      <td>249.0</td>\n",
       "      <td>249.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Dates Action          User         Product  Quantity   Price  \\\n",
       "0 2015-07-01 00:00:01  order    U312622727     P0006944501         1  1069.0   \n",
       "1 2015-07-01 00:00:03  order    U239012343     P0006018073         1  1680.0   \n",
       "2 2015-07-01 00:00:19  order  U10007697373     P0002267974         1   285.0   \n",
       "3 2015-07-01 00:01:10  order    U296328517     P0016144236         1   550.0   \n",
       "4 2015-07-01 00:01:36  order    U300884570  P0014516980122         1   249.0   \n",
       "\n",
       "   Total_Price  \n",
       "0       1069.0  \n",
       "1       1680.0  \n",
       "2        285.0  \n",
       "3        550.0  \n",
       "4        249.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User\n",
       "U166708333      2942744.0\n",
       "U10120098943    1451117.0\n",
       "U142809250       747550.0\n",
       "U1006283751      515688.0\n",
       "U10114715330     456782.0\n",
       "Name: Total_Price, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select User, sum(Total_Price) from orders\n",
    "#gorup by User order by sum(Total_Price) desc limit 5\n",
    "\n",
    "orders.groupby('User')['Total_Price'].sum().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取用户浏览商品纪录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1191486 entries, 0 to 1191485\n",
      "Data columns (total 4 columns):\n",
      "Dates      1191486 non-null datetime64[ns]\n",
      "Action     1191486 non-null object\n",
      "User       1191486 non-null object\n",
      "Product    1191486 non-null object\n",
      "dtypes: datetime64[ns](1), object(3)\n",
      "memory usage: 36.4+ MB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "m_cols = ['Time','Action','User','Product']\n",
    "views = pd.read_csv('Data/purchase_view.tab', sep='\\t',\n",
    "            parse_dates={'Dates': [0]},names=m_cols, encoding='utf-8')\n",
    "views.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Product</th>\n",
       "      <th>buys</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U1000001354</td>\n",
       "      <td>P0014520085</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U10000044910</td>\n",
       "      <td>P0018800250</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U10000056953</td>\n",
       "      <td>P0003926850034</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U10000056953</td>\n",
       "      <td>P0013344760004</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U10000056953</td>\n",
       "      <td>P0013834251</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           User         Product  buys\n",
       "0   U1000001354     P0014520085     1\n",
       "1  U10000044910     P0018800250     1\n",
       "2  U10000056953  P0003926850034     1\n",
       "3  U10000056953  P0013344760004     2\n",
       "4  U10000056953     P0013834251     1"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## SELECT User, Product, COUNT(Product) FROM Orders GROUP BY User, Product\n",
    "orders_cnt = orders.groupby(['User', 'Product'])['Product']\\\n",
    ".count().reset_index(name='buys')\n",
    "orders_cnt.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## SELECT User, Product, COUNT(Product) FROM views GROUP BY User, Product\n",
    "views_cnt = views.groupby(['User', 'Product'])['Product']\\\n",
    ".count().reset_index(name='views')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Product</th>\n",
       "      <th>buys</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U1000001354</td>\n",
       "      <td>P0014520085</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U10000044910</td>\n",
       "      <td>P0018800250</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U10000056953</td>\n",
       "      <td>P0003926850034</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U10000056953</td>\n",
       "      <td>P0013344760004</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U10000056953</td>\n",
       "      <td>P0013834251</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           User         Product  buys\n",
       "0   U1000001354     P0014520085     1\n",
       "1  U10000044910     P0018800250     1\n",
       "2  U10000056953  P0003926850034     1\n",
       "3  U10000056953  P0013344760004     2\n",
       "4  U10000056953     P0013834251     1"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_cnt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Product</th>\n",
       "      <th>views</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>PNone</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U10000000225</td>\n",
       "      <td>P0007107111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U10000000225</td>\n",
       "      <td>P0007107122</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U10000000225</td>\n",
       "      <td>P0007107413</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U10000000225</td>\n",
       "      <td>P0009440001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           User      Product  views\n",
       "0          None        PNone      1\n",
       "1  U10000000225  P0007107111      1\n",
       "2  U10000000225  P0007107122      1\n",
       "3  U10000000225  P0007107413      1\n",
       "4  U10000000225  P0009440001      1"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "views_cnt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 合并购买与浏览纪录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "merge_df = pd.merge(orders_cnt, views_cnt, on=['User','Product'], how='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Product</th>\n",
       "      <th>buys</th>\n",
       "      <th>views</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U10000044910</td>\n",
       "      <td>P0018800250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U10000056953</td>\n",
       "      <td>P0013834251</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U10000065461</td>\n",
       "      <td>P0023597022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U10000092625</td>\n",
       "      <td>P0023729451</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U10000092625</td>\n",
       "      <td>P0024236730</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           User      Product  buys  views\n",
       "0  U10000044910  P0018800250   1.0      2\n",
       "1  U10000056953  P0013834251   1.0      7\n",
       "2  U10000065461  P0023597022   1.0      1\n",
       "3  U10000092625  P0023729451   1.0     14\n",
       "4  U10000092625  P0024236730   1.0      4"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 瞭解使用者在不同日期與時間的消費習慣"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dates</th>\n",
       "      <th>Action</th>\n",
       "      <th>User</th>\n",
       "      <th>Product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-07-01 00:00:01</td>\n",
       "      <td>view</td>\n",
       "      <td>U129297265</td>\n",
       "      <td>P0023468384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-07-01 00:00:03</td>\n",
       "      <td>view</td>\n",
       "      <td>U321001337</td>\n",
       "      <td>P0018926456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-07-01 00:00:05</td>\n",
       "      <td>view</td>\n",
       "      <td>U10070718237</td>\n",
       "      <td>P0000063593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-07-01 00:00:05</td>\n",
       "      <td>view</td>\n",
       "      <td>U393805241</td>\n",
       "      <td>P0005541535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-07-01 00:00:10</td>\n",
       "      <td>view</td>\n",
       "      <td>U10004621134</td>\n",
       "      <td>P0022135540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Dates Action          User      Product\n",
       "0 2015-07-01 00:00:01   view    U129297265  P0023468384\n",
       "1 2015-07-01 00:00:03   view    U321001337  P0018926456\n",
       "2 2015-07-01 00:00:05   view  U10070718237  P0000063593\n",
       "3 2015-07-01 00:00:05   view    U393805241  P0005541535\n",
       "4 2015-07-01 00:00:10   view  U10004621134  P0022135540"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "views.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1191486 entries, 0 to 1191485\n",
      "Data columns (total 4 columns):\n",
      "Dates      1191486 non-null datetime64[ns]\n",
      "Action     1191486 non-null object\n",
      "User       1191486 non-null object\n",
      "Product    1191486 non-null object\n",
      "dtypes: datetime64[ns](1), object(3)\n",
      "memory usage: 36.4+ MB\n"
     ]
    }
   ],
   "source": [
    "views.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015-07-01\n",
       "1    2015-07-01\n",
       "2    2015-07-01\n",
       "3    2015-07-01\n",
       "4    2015-07-01\n",
       "Name: Dates, dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# year, month, day, date, hour \n",
    "views['Dates'].dt.date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dates\n",
       "2015-07-01    24569\n",
       "2015-07-02    17043\n",
       "2015-07-03    28041\n",
       "2015-07-04    28625\n",
       "2015-07-05    15522\n",
       "Name: Action, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select date, count(action) from views\n",
    "# group by date\n",
    "views_cnt_by_date  = views.groupby(views['Dates'].dt.date)['Action'].count()\n",
    "views_cnt_by_date.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "?views_cnt_by_date.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ee9c50>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e6+Xen75JVN+YzLqmFj8Aly3M3hm6toLpLjDGfAH4wgSfWjbBtVFiAeK9562H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\n/x9Wn9+oyQ1CMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9f558d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "views_cnt_by_date.plot(kind='line', figsize=[10,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# select hour(Dates), count(action) from views\n",
    "# group by  hour(Dates)\n",
    "views_cnt_by_hour  = views.groupby(views['Dates'].dt.hour)['Action'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f14fdd8>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tgHnAA1UNeHqfZJyDz5dvr+ouRKSKMnYX8PuPlnJMmzhuG9nZ6zjVMqpXEnec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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bc26b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "views_cnt_by_hour.plot(kind='line', title = 'view count by hour', figsize=[10,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1efdbf28>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u6n8Xz62qh080wTma1uX3PeD3+g73zYBvtdzlB5DuWWHvpHss0VFVdcuA31se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\n1BgLOEmSpMZYwEmSJDXGAk6SJKkx/w8QD+LNM28wtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2134eda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "orders['Total_price'] = orders['Quantity'] * orders['Price']\n",
    "g = orders.groupby('User')['Total_price'].sum().sort_values(ascending=False)[0:10]\n",
    "g.plot(kind=\"bar\", figsize=[10,5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dates\n",
       "2015-07-01    24569\n",
       "2015-07-02    17043\n",
       "2015-07-03    28041\n",
       "2015-07-04    28625\n",
       "2015-07-05    15522\n",
       "Name: Action, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view_daily_cnt = views.groupby(by=views['Dates'].dt.date)['Action'].count()\n",
    "orders_daily_cnt = orders.groupby(by=orders['Dates'].dt.date)['Action'].count()\n",
    "view_daily_cnt.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dates\n",
       "2015-07-01    2324\n",
       "2015-07-02    2059\n",
       "2015-07-03    1778\n",
       "2015-07-04    2166\n",
       "2015-07-05    1413\n",
       "Name: Action, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_daily_cnt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Views</th>\n",
       "      <th>Orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>24569</td>\n",
       "      <td>2324.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-02</th>\n",
       "      <td>17043</td>\n",
       "      <td>2059.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-03</th>\n",
       "      <td>28041</td>\n",
       "      <td>1778.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-04</th>\n",
       "      <td>28625</td>\n",
       "      <td>2166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-05</th>\n",
       "      <td>15522</td>\n",
       "      <td>1413.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Views  Orders\n",
       "2015-07-01  24569  2324.0\n",
       "2015-07-02  17043  2059.0\n",
       "2015-07-03  28041  1778.0\n",
       "2015-07-04  28625  2166.0\n",
       "2015-07-05  15522  1413.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([view_daily_cnt, orders_daily_cnt], axis=1)\n",
    "df.dropna(inplace=True)\n",
    "df.columns = ['Views', 'Orders']\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28341710>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CmoiIiMhxa3NK6R0eQK9Qf5f2QwFNREREhKb1Z+vyXFv/rJkCmoiIiAiQVVTF4ep6l09v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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x30908198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind=\"line\", figsize=[10,5], rot=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
